Rebalance, Ep 16: Hidden Markov Models

Hi everyone, Sherry from QuantConnect here
with your weekly flash briefing, Rebalance. Here’s how QuantConnect is pioneering
tomorrow’s trading with you this week. Documentation was improved this
week by adding a table of contents for each page. Allowing you to find
topics you’re looking for faster. We’re also installing new search technology
which will help you find relevant documentation. Gerardo was hard at work
making demonstration research notebooks for all the alternative data vendors we
support. To improve usability for pandas users we’ve improved pandas data series
access. Now history results can use a Symbol object as a key while using the
iloc and loc indexers. You’ll see a reduction in the frequency of timeouts
with Interactive Brokers data requests for futures and options. Our team worked
to request the right tick types and more intelligently handle rate limit errors
from the IBGateway. In this week’s “From Research to Production” post Jack walks
us through a strategy using Hidden Markov Models to represent the market
shift from bull to bear and to predict the market state. A Markov process is
stochastic where the possibility of switching to another state depends only
on the current state of the model. Dive into the concept and clone the
template in the link below. Like staying updated on the latest QC
news? Throw us a thumbs up and subscribe. Thanks for listening to this week’s
Rebalance and happy coding!


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